Graphics Processing Units (GPUs) are essential for general-purpose applications and are commonly leveraging multi-level caches to alleviate memory access pressure. However, the default cache management may lose opportunities for optimal performance in different applications. Although existing cache bypassing techniques tend to address this challenge, these methods predominantly concentrate on single-level cache, thus restricting their potential for further enhancements. To mitigate this issue, we propose Mpache, a novel software-based mechanism designed to bypass multi-level caches based on the characterization of load instructions. Mpache constructs an interaction graph and analyzes the cooperation and contention among instructions. Then, the profiling data of bypassing effectiveness guides Mpache to select the appropriate cache levels to bypass for each instruction. Finally, the design is integrated into the compiler to enable automatic bypassing for existing workloads. Evaluations on off-the-shelf GPUs show that Mpache achieves an average 1.15× speedup over the default cache policy, and effectively outperforms prior arts.

GPUs have been heavily utilized in diverse applications, and numerous approaches, including kernel fusion, have been proposed to boost GPU efficiency through concurrent kernel execution. However, these approaches generally overlook the opportunities to mitigate warp stalls and improve instruction level parallelism (ILP) in inter-kernel resource sharing. To address this issue, we introduce GOPTX, a novel design for kernel fusion that improves ILP through deliberate weaving instructions at the PTX level. GOPTX establishes a merged control flow graph (CFG) from original kernels, enabling to interleaving of instructions that were sequentially executed by default and minimizing pipeline stalls on data hazards. We further propose a latency-aware instruction weaving algorithm for more efficient instruction scheduling and an adaptive code slicing method to enlarge the scheduling space. Experimental evaluation demonstrates that GOPTX achieves an average speedup of $\mathbf{1 1. 2 \%}$ over the baseline concurrent execution, with a maximum improvement of 23%. The hardware resource utilization statistics show significant enhancements in eligible warps per cycle and resource use.